Feature density object classification, systems and methods

ABSTRACT

A system capable of determining which recognition algorithms should be applied to regions of interest within digital representations is presented. A preprocessing module utilizes one or more feature identification algorithms to determine regions of interest based on feature density. The preprocessing modules leverages the feature density signature for each region to determine which of a plurality of diverse recognition modules should operate on the region of interest. A specific embodiment that focuses on structured documents is also presented. Further, the disclosed approach can be enhanced by addition of an object classifier that classifies types of objects found in the regions of interest.

This application claims priority to U.S. provisional application61/913,681 filed Dec. 9, 2013. U.S. provisional application 61/913,681and all other extrinsic references mentioned herein are incorporated byreference in their entirety.

FIELD OF THE INVENTION

The field of the invention is object recognition and classificationtechnologies.

BACKGROUND

The following description includes information that may be useful inunderstanding the present invention. It is not an admission that any ofthe information provided herein is prior art or relevant to thepresently claimed invention, or that any publication specifically orimplicitly referenced is prior art.

Many object recognition technologies have been developed since theadvent of digital acquisition techniques. One example technique that canbe used to identify objects that might appear in a digital imageincludes Scale-Invariant Feature Transform (SIFT) as discussed in U.S.Pat. No. 6,711,293 to Lowe titled “Method and Apparatus for IdentifyingScale Invariant Features in an Image and Use of the Same for Locating anObject in an Image”, filed Mar. 6, 2000. Typically, only one algorithmis applied to a digital representation of a scene to identify or locatean object within the digital representation. Although useful foridentifying objects that are amenable to the specific philosophicalfoundations of the algorithms, such a single minded approach is lessthan efficient across many different classes of objects; different typesof objects across which there can be a high variability in featuredensity.

Some effort has been applied toward detecting object features. Forexample, U.S. Pat. No. 5,710,833 to Moghaddam et al. titled “Detection,Recognition and Coding of Complex Objects using Probabilistic EigenspaceAnalysis”, filed Apr. 20, 1995, describes calculating probabilitiesdensities associated with an image or portions of an image to determineif an input image represents an instance of an object. Still, Moghaddamonly offers a single approach for identifying objects and fails toprovide insight into classification of objects.

Substantial effort toward image processing as been applied in the fieldof medical imaging. European patent specification EP 2 366 331 toMiyamoto titled “Radiation Imaging Apparatus, Radiation Imaging Method,and Program”, filed Mar. 1, 2011, references calculating image densitywithin a radioscopic image and selectively executing an extractionalgorithm for reach region of interest where the density informationreflects tissue density. The extraction algorithm results in featuresthat can aid in analysis of corresponding tissue.

U.S. Pat. No. 8,542,794 also to Miyamoto titled “Image ProcessingApparatus for a Moving Image of an Object Irradiated with Radiation,Method Thereof, and Storage Medium”, filed Mar. 3, 2011, also discussesimage processing with respect to radioscopic imaging. Miyamoto discussescapturing a “feature amount” from pre-processed moving images where the“feature amounts” represent values derived from the image data. Thus,the feature amounts can reflect aspects of image data related to regionin an image.

U.S. Pat. No. 8,218,850 to Raundahl et al. titled “Breast Tissue DensityMeasure” filed Dec. 23, 2008, makes further progress in the medicalimaging field of extracting tissue density information from radioscopicimages. Raundahl describes driving a probability score from the tissuedensity information and that indicates that a mammogram image is amember of a predefine class of mammograms images. Miyamoto and Raundahloffer useful instructions toward processing medical image data based onextracted features. However, such approaches are not applicable to abroad range of object types, say shoes, animals, or structureddocuments.

U.S. patent application publication 2008/0008378 to Andel et al. titled“Arbitration System for Determining the Orientation of an Envelope froma Plurality of Classifiers”, filed Jul. 7, 2006; and U.S. patentapplication publication 2008/0008379 also to Andel et al. titled “Systemand Method for Real-Time Determination of the Orientation of anEnvelope”, filed Jul. 7, 2007, both describe using a classifier thatdetermines an orientation of an envelope based on an image of theenvelope. The orientation classifier operates as a function of pixeldensity, (i.e., regions having dark pixels).

U.S. Pat. No. 8,346,684 to Mirbach et al. titled “Pattern ClassificationMethod”, filed internationally on Jul. 17, 2007, describes identifyingtest patterns in a feature space based on using a density function.During an on-line process, patterns can be classified as belonging toknown patterns based on the known patterns having similar densityfunctions.

International patent application publication WO 2013/149038 toZouridakis titled “Method and Software for Screening and Diagnosing SkinLesions and Plant Diseases” filed Mar. 28, 2013, also describes aclassification system. Zouridakis discusses extracting features fromregions within an object boundary in an image and comparing theextracted features to known object features in a support vector machine(SVM). The SVM returns a classification of the object.

Further, U.S. Pat. No. 8,553,989 to Owechko et al. titled“Three-Dimensional (3D) Object Recognition System Using Region ofInterest Geometric Features”, filed Apr. 27, 2010, uses a feature vectorto classify objects of interest. Shape features are calculated byconverting raw point cloud data into a regularly sampled populateddensity function where the shape features are compiled into the featurevector. The feature vector is then submitted to a multi-class classifiertrained on feature vectors.

U.S. Pat. No. 8,363,939 to Khosla et al. titled “Visual Attention andSegmentation System”, filed Jun. 16, 2008, discusses applying a floodingalgorithm to break apart an image into smaller proto-objects based onfeature density where the features represent color features derivedbased on various color channels. Unfortunately, Khosla merely attemptsto identify regions of high saliency, possibly growing the region,rather than attempting differentiate among objects distributed acrossregions of interest.

U.S. patent application publication 2013/0216143 to Pasteris et al.titled “Systems, Circuits, and Methods for Efficient Hierarchical ObjectRecognition Based on Clustered Invariant Features”, filed Feb. 7, 2013,describes extracting key points from image data and grouping the keypoints into clusters that enforce a geometric constraint. Some clustersare discarded while the remaining clusters are used for recognition.Interestingly, Pasteris seeks to discard low density sets and fails toappreciate that feature density, regardless of its nature, can representrich information.

International patent application WO 2007/004868 to Geusebroek titled“Method and Apparatus for Image Characterization”, filed Jul. 3, 2006,seeks to characterize images based on density profile information. Thesystem analyzes images to find color or intensity transitions. Thedensity profiles are created from the transitions and fitted topredefined parameterization functions, which can be used to characterizethe image.

U.S. Pat. No. 8,429,103 to Aradhye et al. titled “Native MachineLearning Service for User Adaptation on a Mobile Platform”, filed Aug.2, 2012; and U.S. Pat. No. 8,510,238 titled “Method to Predict SessionDuration on Mobile Device Using Native Machine Learning”, filed Aug. 14,2012, both describe a machine learning service that seeks to classifyfeatures from image data.

All publications herein are incorporated by reference to the same extentas if each individual publication or patent application werespecifically and individually indicated to be incorporated by reference.Where a definition or use of a term in an incorporated reference isinconsistent or contrary to the definition of that term provided herein,the definition of that term provided herein applies and the definitionof that term in the reference does not apply.

The above cited references offer various techniques for applying someform of algorithm to image data to identify objects represented withinthe image data. Still, the collective references rely on a singlealgorithm approach to identify features within regions of interest. Thereferences fail to appreciate that each region of interest could have adifferent type or class of object (e.g., unstructured documents,structured documents, faces, toys, vehicles, logos, etc.) from the otherregions. Further, the references fail to provide insight into how suchdiverse regions of interest could be processed individually or how todetermine which type of processing would be required for such regions.Thus, there is still a need for systems capable of determining whichtype of processing should be applied to identified regions of interest.

In some embodiments, the numbers expressing quantities of ingredients,properties such as concentration, reaction conditions, and so forth,used to describe and claim certain embodiments of the invention are tobe understood as being modified in some instances by the term “about.”Accordingly, in some embodiments, the numerical parameters set forth inthe written description and attached claims are approximations that canvary depending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the invention are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable. The numerical values presented in some embodiments of theinvention may contain certain errors necessarily resulting from thestandard deviation found in their respective testing measurements.

As used in the description herein and throughout the claims that follow,the meaning of “a,” “an,” and “the” includes plural reference unless thecontext clearly dictates otherwise. Also, as used in the descriptionherein, the meaning of “in” includes “in” and “on” unless the contextclearly dictates otherwise.

The recitation of ranges of values herein is merely intended to serve asa shorthand method of referring individually to each separate valuefalling within the range. Unless otherwise indicated herein, eachindividual value is incorporated into the specification as if it wereindividually recited herein. All methods described herein can beperformed in any suitable order unless otherwise indicated herein orotherwise clearly contradicted by context. The use of any and allexamples, or exemplary language (e.g. “such as”) provided with respectto certain embodiments herein is intended merely to better illuminatethe invention and does not pose a limitation on the scope of theinvention otherwise claimed. No language in the specification should beconstrued as indicating any non-claimed element essential to thepractice of the invention.

Groupings of alternative elements or embodiments of the inventiondisclosed herein are not to be construed as limitations. Each groupmember can be referred to and claimed individually or in any combinationwith other members of the group or other elements found herein. One ormore members of a group can be included in, or deleted from, a group forreasons of convenience and/or patentability. When any such inclusion ordeletion occurs, the specification is herein deemed to contain the groupas modified thus fulfilling the written description of all Markushgroups used in the appended claims.

SUMMARY OF THE INVENTION

The inventive subject matter provides apparatus, systems and methods inwhich an object data processing system can, in real-time, determinewhich recognition algorithms should be applied to regions of interest ina digital representation. One aspect of the inventive subject matterincludes a system comprising a plurality of diverse recognition modulesand a data preprocessing module. Each module represents hardwareconfigured to execute one or more sets of software instructions storedin a non-transitory, computer readable memory. For example, therecognition modules can comprise at least one recognition algorithms(e.g., SIFT, DAISY, ASR, OCR, etc.). Further, the data preprocessingmodule can be configured, via its software instructions, to obtain adigital representation of a scene. The digital representation caninclude one or more modalities of data including image data, video data,sensor data, news data, biometric data, or other types of data. Thepreprocessing module leverages an invariant feature identificationalgorithm, preferably one that operates quickly on the target data, togenerate a set of invariant features from the digital representation.One suitable invariant identification feature algorithm that can beapplied to image data includes the FAST corner detection algorithm. Thepreprocessing module further clusters or otherwise groups the set ofinvariant features into regions of interest where each region ofinterest can have an associated region feature density (e.g., featuresper unit area, feature per unit volume, feature distribution, etc.). Thepreprocessor can then assign each region one or more of the recognitionmodules as a function of the region's feature density. Each recognitionmodule can then be configured to process their respective regions ofinterest according the recognition module's recognition algorithm.

Various objects, features, aspects and advantages of the inventivesubject matter will become more apparent from the following detaileddescription of preferred embodiments, along with the accompanyingdrawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an object data preprocessing ecosystem.

FIG. 2 presents an illustrative example of a digital representationdepicting a scene, as used in processing according to aspects of theinventive subject matter.

FIG. 3 presents an illustrative example of features generated for thescene of FIG. 2 .

FIG. 4 illustrates clusters generated for the generated features of thescene.

FIG. 5 provides an illustrative examples of the regions of interestcorresponding to the clusters of FIG. 4 , and examples of featuredensities corresponding to the regions of interest.

FIG. 6 illustrates the assigned recognition modules according to thefeature densities of FIG. 5 .

FIG. 7 presents an overview of a system capable of processing documentsfrom video frames.

FIG. 8 illustrates a data flow for a possible video-based text detectionmodule.

FIG. 9 provides an example of a resulting set of features based onapplying a FAST corner detection algorithm to an image of printed media.

FIGS. 10A-10C illustrate converting features in a region of interestinto feature density information in the form of a binary corner densitymap.

FIGS. 11A-11C provide examples of various substructures within a regionof interest where the substructure and associated attributes are derivedfrom feature density information.

FIG. 12 illustrates a block diagram of an audio feedback module.

FIGS. 13A-13C illustrate presenting audio and visual feedback to a userindicating if a text region is in an appropriate spot.

FIG. 14 illustrates a block diagram of still-image capture, OCR, and TTSmodules.

FIGS. 15A-15C presents actual display screens for still images, OCRmodule interactions, and generated text from a region of interest.

DETAILED DESCRIPTION

Throughout the following discussion, numerous references will be maderegarding servers, services, interfaces, engines, modules, clients,peers, portals, platforms, or other systems formed from computingdevices. It should be appreciated that the use of such terms is deemedto represent one or more computing devices having at least one processor(e.g., ASIC, FPGA, DSP, x86, ARM, ColdFire, GPU, multi-core processors,etc.) configured to execute software instructions stored on a computerreadable tangible, non-transitory medium (e.g., hard drive, solid statedrive, RAM, flash, ROM, etc.). For example, a server can include one ormore computers operating as a web server, database server, or other typeof computer server in a manner to fulfill described roles,responsibilities, or functions. One should further appreciate thedisclosed computer-based algorithms, processes, methods, or other typesof instruction sets can be embodied as a computer program productcomprising a non-transitory, tangible computer readable media storingthe instructions that cause a processor to execute the disclosed steps.The various servers, systems, databases, or interfaces can exchange datausing standardized protocols or algorithms, possibly based on HTTP,HTTPS, AES, public-private key exchanges, web service APIs, knownfinancial transaction protocols, or other electronic informationexchanging methods. Data exchanges can be conducted over apacket-switched network, the Internet, LAN, WAN, VPN, or other type ofpacket switched network.

The following discussion provides many example embodiments of theinventive subject matter. Although each embodiment represents a singlecombination of inventive elements, the inventive subject matter isconsidered to include all possible combinations of the disclosedelements. Thus if one embodiment comprises elements A, B, and C, and asecond embodiment comprises elements B and D, then the inventive subjectmatter is also considered to include other remaining combinations of A,B, C, or D, even if not explicitly disclosed.

As used herein, and unless the context dictates otherwise, the term“coupled to” is intended to include both direct coupling (in which twoelements that are coupled to each other contact each other) and indirectcoupling (in which at least one additional element is located betweenthe two elements). Therefore, the terms “coupled to” and “coupled with”are used synonymously.

The following subject matter is directed toward systems that processdigital representations of a scene to identify one or more objects orclasses of objects. Previous techniques are slow and are unsuitable foruse in embedded devices having limited resources or merely apply asingle processing technique for all purposes. For example, a processingmodule might a priori assume that a target object of interest is adocument and attempt to apply optical character recognition to theobject regardless of whether or not the object is a document.

The Applicants have come to appreciate that each type of processingtechnique has an underlying philosophical approach to analyzing digitaldata when identifying patterns or objects and that each philosophicalapproach does not necessarily work across a broad spectrum of objecttypes or classes. Consider a scenario were the digital representationencodes an image of a logo. Logos typically lack texture or features,which render recognition techniques based on SIFT less useful. However,edge detection techniques might be more useful because a correspondingrecognition module can construct edges or boundaries associated with thelogo and search for similar known objects based on the constructed edgesor their corresponding edge descriptors.

Still, it is very difficult for computing systems to determine whichtype of recognition technique should be applied to a digitalrepresentation in order to extract object related information withoutactually applying each technique separately. Such an approach would bevery computationally intensive and resource heavy, which would exceedthe patience and good will of a consumer market.

The Applicants have further appreciated that one can quickly determineregions of interest within a digital representation (e.g., video data,video frame, image data, audio sample, documents, etc.) and quicklydetermine how to differentiate the regions of interest with respect tomore optimal recognition techniques. As described below the Applicantshave found that one can apply a preprocessing feature identificationalgorithm to a digital representation to identify regions of interest.The results of the feature identification algorithm include features,descriptors for example, that indicate areas of interest. Each region orarea would have a characteristic feature density, which itself wouldhave a signature that can be an indicator of what type of additionalprocessing would be required. Thus, the Applicants have appreciated thatthere can be correlations among feature density signatures from a firstrecognition algorithm and classes of additional, different recognitionalgorithms.

FIG. 1 presents an example ecosystem 100 that preprocesses digital datato determine how various regions of interest should be furtherprocessed. Contemplated ecosystems include an object data preprocessingsystem 110 that quickly analyzes digital representations 121 of a scene140. The disclosed system 100 is able to process video data fromexisting cell phones as frame rate (i.e., a series of still frames,including frame rate information). Although the following discussion ismainly presented with respect to an image data modality, it should beappreciated that other data modalities (e.g., video, audio, sensor data,etc.) could benefit from the presented techniques.

The object data processing system 110 comprises a plurality of diverserecognition modules (labeled A-N) and at least one data preprocessingmodule 120. One should appreciate that the individual components of theobject data processing system 110 and/or ecosystem 100 can be housed ina single device (e.g., tablet, smart phone, server, game console, GoogleGlass, ORCAM® camera, etc.) or distributed across multiple devices. Forexample, the feature identification algorithms 122 might reside on asmart phone (which can also include or not include a sensor 130 such asa camera) while one or more remote servers house the various recognitionmodules A-N.

In the example shown, one or more sensors 130 acquire sensor data thatform a digital representation 121 of a scene 140. The sensors 130 caninclude a wide variety of device types including cameras, microphones,Hall probes, thermometers, anemometers, accelerometers, touch screens,or other components or devices that capture sensor data. In view thatthe sensors 130 could include a broad spectrum of device types, theresulting sensor data as well as the digital representation 121 of thescene can include a broad spectrum of data modalities such as imagedata, audio data, biometric data, news data, temperature data, pressuredata, location data, electrical data, or other types of data.

Each recognition module A-N from the set of recognition modules cancomprise one or more recognition algorithms. In embodiments, therecognition modules A-N are classified according to their respectivealgorithm's underlying philosophical approach (e.g., what types offeature arrangements and pixel arrangements are sensitive to aparticular algorithm, what types of recognition or recognitionconditions a particular algorithm is best suited to, etc.) toidentifying objects. Example types of algorithms can include a templatedriven algorithm, a face recognition algorithm, an optical characterrecognition algorithm, a speech recognition algorithm, an objectrecognition algorithm, edge detection algorithm, corner detectionalgorithm, saliency map algorithm, curve detection algorithm, a textonidentification algorithm, wavelets algorithm, or other class ofalgorithms. For example, an audio recognition module might have anautomatic speech recognition (ASR) algorithm and a support vectormachine (SVM)-based algorithm. In more preferred embodiments, eachrecognition module would likely have a single recognition algorithm sothat each module can individually function in parallel on multi-threadedor multi-core system to support parallelism during actual processing.

Each recognition module A-N can further comprise feature densityselection criteria that represent characteristics indicative of when therecognition module's corresponding recognition algorithm would beconsidered applicable. The feature density selection criteria includerules, requirements, optional conditions, or other factors defined basedon feature density attributes. It should be appreciated that suchattributes can be specific to a particular feature identificationalgorithm. For example, SIFT recognition module A might have twoseparate feature density selection criteria, one selection criteriamight be relevant when the feature identification algorithm 122 is FASTcorner detection and the other selecting criteria might be relevant whenthe feature identification algorithm 122 is MSER. Each selectioncriteria could have widely different characteristics depending on thecorresponding feature identification algorithm used for preprocessing.Example feature identification algorithms 122 preferably yield invariantfeatures that are invariant with respect to one or more of scale,translation, orientation, affine transforms, skew, speculation,background noise, or other effects. More specific examples of invariantfeature identification algorithms 122 include FAST, SIFT, FREAK, BRISK,Harris, DAISY, or MSER. In yet more preferred embodiments, the featureidentification algorithm 122 is selected to be faster with respect toprocessing the digital representation 121 relative to the recognitionalgorithms in the recognition modules A-N. Further, the featureidentification algorithm could also be drawn from the same classes ofalgorithms are the recognition modules; for example, an edge detectionalgorithm, a corner detection algorithm, a saliency map algorithm, acurve detection algorithm, a texton identification algorithm, a waveletsalgorithm, etc.

In the example shown, the data preprocessing module 120 obtains adigital representation 121 of the scene 140. The digital representation121 can be obtained through various data communication techniques. Inembodiments, the digital representation 121 can be obtained directlyfrom sensor 130. In embodiments, the digital representation 121 can bestored in a common memory on the same device (e.g., a cell phonememory). In embodiments, the digital representation 121 might beobtained via a web service or through one or more known protocols (e.g.,FTP, HTTP, SSH, TCP, UDP, etc.). The manner in which the digitalrepresentation 121 is obtained can vary depending on the embodiment ofthe inventive subject matter and/or the configuration of the variouscomponents of the ecosystem 100.

FIG. 2 provides an example of a digital representation 200 of a scene(corresponding to digital representation 121 of scene 140 of FIG. 1 ).In the example of FIG. 2 , the digital representation 200 is consideredto be an image (such as a digital still image or frame of video) of thescene 140. As shown in FIG. 2 , the digital representation 200 depicts ascene that includes a building 210 (including windows 211 and a door212), a person 220 (showing the person's face 221 including eyes andmouth as well as the upper part of their body 222 including their torsoand arms) and a billboard 230 (which includes a base post 231, a displaysurface 232 and text 233 depicted within the display surface 232).

The data preprocessor module 120 generates a set of invariant features123 by applying the invariant feature identification algorithm 122 tothe digital representation 121. Examples of invariant features caninclude descriptors, key points, edge descriptors, or other types offeatures.

FIG. 3 illustrates the generated set of invariant features 123 for thedigital representation 200 of FIG. 2 , resulting from the application ofinvariant feature identification algorithm 122 to by the datapreprocessor module 120. In FIG. 3 , each individual invariant feature310 is depicted as a bold circle, generated by the invariant featureidentification algorithm throughout the digital representation 200. Itshould be noted that the set of invariant features 310 in FIG. 3 is anillustrative example rather than an exact representation of the resultsof a particular invariant feature identification algorithm 122. Thus, itis appreciated that for a particular invariant feature algorithm 122,the amount of features and their locations can vary based on a number offactors including the quality and characteristics of the digitalrepresentation. Similarly, the amount of generated features and theirlocations generated by various invariant feature algorithms 122 candiffer for the same digital representation.

Generally speaking, the feature identification algorithms generatefeatures based on variations or differences between the characteristicsof different pixels within an image. While different featureidentification algorithms may have different philosophical approaches togenerating features that make different pixel arrangements sensitive toa particular algorithm (e.g., FAST looks for corners whereas SIFT looksfor gradients), in general a degree of variation between pixels in animage is needed to generate the features. Correspondingly, sections ofan image with little to no pixel variation are generally less likely togive rise to generated invariant features than those with greater pixelvariation. Thus, shown in FIG. 3 , the “objects” (the building, personsign post, as well as the horizon) in the image are shown to have morefeatures 310 than the relatively uniform area above the horizon (here, acloudless, clear sky) or below the horizon (here, a relatively visuallyuniform ground). However, features may still be generated in these areasdue to factors that might cause pixels to differ from those in theotherwise uniform area such as image data errors, artifacts of a lensused (e.g. distortion, filters, dirt or scratches on the lens, glare,etc.). Nevertheless, as illustrated in FIG. 3 , these features willgenerally be of a reduced number relative to the features generatedbecause of the larger pixel differences in the “objects” in the image.

Each feature 310 can include a coordinate with respect to the digitalrepresentation 200. With respect to an image, the feature coordinatescan comprises a pixel coordinate (x, y) in the image. With respect tovideo data or audio data, the coordinates could also include a timecomponent, a frame count component or other component indicative of atemporal location of the pixel within the video or audio data relativeto the beginning, ending or other reference point within the video oraudio data. In some embodiments, the coordinates can be with respect toa multi-dimensional feature space or descriptor space rather than withrespect to the digital representation 121.

Once the set of invariant features 123 has been generated, the datapreprocessor module 120 can proceed to cluster the set of invariantfeatures 123 into regions of interest in the digital representation ofthe scene. In some embodiments, the data preprocessor module 120 canapply one or more clustering algorithms to the set of invariant features123 to generate clusters. Examples of suitable clustering algorithmsinclude K-means clustering algorithms, EM clustering algorithms, orother types of clustering algorithms. FIG. 4 illustrates the clusters410,420,430,440 generated for the set of invariant features 123generated in FIG. 3 for the digital representation 200 by the datapreprocessor module 120 via the clustering algorithm(s). It should benoted that generated clusters of features can overlap. For example,cluster 420 and cluster 430 in FIG. 4 have a degree of overlap.

Having identified clusters 410,420,430,440 of features 310, the datapreprocessing module 120 can partition the space in which the clustersreside such that each partitioned portion of the space represents aregion of interest 124. FIG. 5 shows an illustrative example of regionsof interest 510,520,530,540 corresponding to each cluster410,420,430,440, respectively.

As described above, invariant features 310 tend to be generated ingreater numbers and density for areas with greater pixel variations.These areas can correspond to objects (or sections of objects) and/ortext of interest in a scene. Because the regions of interest 124correspond to clusters reflective of the distribution of features 310 ina scene according to these pixel variations, the regions of interest 124can, in embodiments, be considered to represent physical objects (orportions thereof) and/or text in the scene.

In some image-based embodiments (such as the one illustrated in FIG. 5), the region of interest can correspond to a bounding box thatsubstantially surrounds, to within thresholds, the correspondingcluster. In view that dusters can be close to each other (or evenoverlap), such bounding boxes could overlap each other. For example, inFIG. 5 , the bounding boxes corresponding to regions of interest 520 and530 are shown to overlap.

In other embodiments, the partitioned regions of interest 124 couldinclude shapes (e.g., circles, ellipses, etc.), volumes (e.g., sphere,rectilinear box, cone, etc.) or even higher dimensional shapes. Thespace does not necessarily have to be tessellated into regions ofinterest, but could be tessellated via Voronio decomposition if desired.Thus, the digital representation 121 can be decomposed into regions ofinterest 124 having clusters of invariant features.

An alternative approach to identifying a region of interest 124 caninclude configuring the preprocessing module 120 to require a set numberof features 310 per region and then scaling the region's boundaries sothat the region 124 has the required number of features 310. Forexample, if the number of features is set to a value of 20 for example,the bounding box around a representation of a human face in an imagemight be relatively larger, perhaps several hundred pixels on a side.However, the bounding box around text having 20 features might berelatively small, perhaps just a few tens of pixels on the side. Theinventive subject matter is therefore considered to include adjustingthe boundary conditions of a region of interest 124 to enforce a featurecount.

The clusters of invariant features 310 within each region of interestcan take on different forms. In some embodiments, the clusters couldrepresent a homogeneous set of invariant features. For example, whenonly FAST is used during preprocessing, the clusters will only includeFAST descriptors. Still, it other embodiments, more than one invariantfeature identification algorithm could be applied during preprocessingin circumstances where there are sufficient computing resources. In suchcases, the clusters could include a heterogeneous set of invariantfeatures (e.g., FAST and FREAK) where each type of feature can providedifferentiating information (e.g., scale, orientation, etc.).

The data preprocessing module 120 can be programmed to filter invariantfeatures 310 within a cluster, or across all clusters, based on one ormore quality measures. For example, in embodiments that yield a saliencymeasure, the saliency measure can be used to reduce or otherwise modifythe set of invariant features to include features of most merit. Inthese embodiments, a principle component analysis (PCA) can be used on atraining image set to determine which dimensions of a descriptor orfeature offer the greatest discriminating power among known objects inthe training set. The resulting principle components yield values thatindicate which dimensions have the most variance. In such scenarios thesaliency measure can include a metric derived based on which featureshave values in dimensions having the greatest variances. In one example,the saliency metric can include a simple number indicating whichdimensions with non-zero values in a feature (such as a SIFT descriptor)correspond to the principle components generated by the PCA. It shouldbe appreciated that the modification of set of invariant features canoccur before clustering or after clustering. Consider a scenario whereFAST is used as a preprocessing feature identification algorithm. TheFAST features can be filtered based on the saliency measure beforeclustering begins. Alternatively, the clusters can first be identified,and then analyze the saliency measures of the FAST features within eachcluster to aid during classification. In these situations, a low-averagesaliency measure (e.g., a number indicating that a corresponding featureis not likely to be very useful in the analysis) of a cluster can be anindication of a 3D object while a relatively high-average saliencymeasure (e.g., a large number indicating the corresponding feature islikely to be useful in the analysis) of a cluster can indicate a regionof text.

Each type of descriptor or feature resulting from the invariant featureidentification algorithm can carry additional information beyond merelyrepresenting a descriptor. Such additional metadata can be consideredreflective the feature identification algorithm's underlyingassumptions. FAST generates a large number of descriptors, which isuseful for fast region identification but does not necessarily provideadditional information. SIFT, on other hand, albeit somewhat slower thanFAST generates descriptors that provide orientation, scale, saliency, orother information, which can aid in region identification orclassification. For example, a text region would likely have a certainnumber of features that relate to a specific scale. Orientationinformation can aid in determining how best to orient the text regiongiven the number of features and information from the associateddescriptors in the region. SIFT is sometimes more advantageous than FASTin embodiments that would use SIFT for generic object recognition laterin the analysis stream.

Each region of interest has one or more clusters distributed within theregion's corresponding partitioned portion of the space. The region'slocal space could be an area within an image (e.g., px{circumflex over( )}2 (area of pixels squared), cm{circumflex over ( )}2, etc.), avolume (e.g., cm{circumflex over ( )}2*time, cm{circumflex over ( )}3,etc.), or other volume. Further, each region can have a correspondingregion feature density that is characterized by the nature of thecluster of invariant features distributed over the region of interest124's space.

In embodiments, the preprocessing module 120 can be programmed toconsider only clusters and/or regions of interest having at least aminimum feature density and to discard or filter out clusters or regionsof interest whose density falls below the minimum feature densitythreshold. The minimum feature density threshold can be a thresholdcorresponding to the minimum density necessary for any of therecognition algorithms to be able to perform recognition at anacceptable rate.

In embodiments, the feature density of a region of interest 124 can bein the form of a simple scalar density metric such as a raw densitycomprising the number of features 310 of a region divided by the area(or volume) of the region's corresponding space. Further, as discussedabove, the region feature density can be representative or reflective ofa homogeneous set of invariant features or a homogeneous set ofinvariant features depending on how many invariant featureidentification algorithms 122 are applied during preprocessing.

The region feature density can further comprise additional values orstructure beyond a simple scalar density metric, especially depending onthe nature of the duster within the region of interest. In someembodiments, the distribution of features within the cluster or withinthe region could include feature substructure. Example substructure caninclude multiple smaller dusters, a sub-cluster of invariant features, aperiodicity of invariant features, a block structure of invariantfeatures, a frequency of invariant features, a low density region ofinvariant features, patterns, contours, variance, distribution widths,type of distribution (e.g., Gaussian, Poisson, etc.), centroids, orother types of structure.

The data preprocessing module 120 utilizes each region of interest'sregion feature density to determine which type or types of recognitionalgorithms would likely be efficient to apply to the region. The datapreprocessing module 120 assigns each region of interest 124 at leastone of the recognition module(s) A-N as a function of the region featuredensity (of the region) and one or more feature density selectioncriteria 125 associated with the recognition modules A-N. Inembodiments, the selection of the recognition module(s) A-N for a regionof interest 124 can also be as a function of the invariant featuresubstructure.

In embodiments, the preprocessing module 120 can access a database orlookup table of recognition modules (stored in a non-transitory computerreadable storage medium that can be a part of or accessible to theobject data processing system 110) that is indexed according thestructure, substructure, or other region feature densitycharacteristics. For example, the database or lookup table could indexthe recognition modules A-N by raw feature density (or a range of rawfeature density values associated with each recognition module). Oneshould note that each recognition module A-N can also be multi-indexedaccording to the various characteristics (e.g., type of distribution,contour information, etc.). In this embodiment, the indexing system canbe considered the selection criteria 125. In embodiments, eachrecognition module A-N can include metadata that represents its specificfeature density selection criteria 125.

The feature density selection criteria 125 can include variousparameters, requirements, rules, conditions, or other characteristicsthat outline the feature-density-based context to which a particularrecognition module is considered relevant. As stated above, such acontext would likely be different for each feature identificationalgorithm of the modules A-N used in processing. The “feature density”upon which the feature density selection criteria 125 can be defined ina plurality of forms. It is contemplated the criteria 125 can includerules that operate as a function of feature densities such as featuresper unit time, feature per unit area (e.g., units of pixels squared),features per geometrical area (e.g., #features/cm{circumflex over( )}2), features per unit volume, features per pixels squared times adepth of field (e.g., a derived volume), feature per unit geometricvolume (e.g., #features/cm{circumflex over ( )}3), or other densitycalculation. Additionally, the selection criteria 125 could include alow density threshold possibly representing a minimum density (i.e., theminimum density necessary for a particular recognition module to beeffective or to be preferable over other recognition modules), highdensity threshold possibly representing a maximum density (i.e., themaximum density for which a particular recognition module is consideredto be effective or preferable over other recognition modules), and/or afeature density range applicable for each recognition module A-N (i.e.,the feature density range between a minimum and maximum in which aparticular recognition module is deemed most effective and/or preferredover other available recognition modules).

Feature density thresholds can be used to categorize ranges of featuredensities and thus narrow down potential applicable modules forselection. The categorization can be reflective of the underlyingphilosophical approaches of types of modules, such that the propercategories (along these philosophies) can be pre-selected bypreprocessing module 120 prior to the selection of the actual modules toemploy. For example, a feature density threshold can be used to classifydensities above a particular value as “high density” and below the valueas “low density.” For example with respect to image data, if a FASTalgorithm discovers a low density region, then this might indicate aregion of interest 124 that would best be served by an edge-detectionalgorithm because the region is texture-less. However, if the FASTalgorithm identifies region of interest having a high feature density,then the region of interest might require a SIFT-based algorithm. Stillfurther, if the feature density falls within a range, the region ofinterest might be better served by an OCR algorithm because the range isconsistent with text.

Returning to the example of FIG. 5 , the preprocessing module 120 isprogrammed to calculate feature densities 511,521,531,541 associatedwith regions of interest 510,520,530,540, respectively (collectivelyreferenced as feature densities 501). As shown in FIG. 5 , featuredensities 511,521,531,541 each have their respective feature densityvalues “A”, “B”, “C” and “D”. For this example, the feature densities501 are considered to be a raw density of a number of features per areaunit.

Having calculated the feature densities 501 for all of the regions ofinterest, the preprocessing module 120 proceeds to apply the featuredensities 501 for each region of interest 510-540 to the feature densityselection criteria 125 for each of the recognition modules A-N. In thisexample, it is assumed that the feature density selection criteria 125for each of the recognition modules A-N includes a feature density rangefor which each recognition module is deemed the “correct” module and assuch, each of the values “A”, “B”, “C” and “D” will fall within thefeature density range of at least one of the recognition modules A-N (asdescribed above, dusters or regions of interest below a minimum featuredensity can be filtered out; it is assumed that in the example of FIG. 5, all of the regions of interest are above the minimum feature density).

The preprocessing module 120 proceeds to determine that the value “D”(corresponding to feature density 541 of region of interest 540) fallswithin the feature density selection criteria for recognition module C(an OCR recognition module), as the feature density reflects thattypically found in text. Similarly, preprocessing module 120 proceeds todetermine that the value “C” (corresponding to feature density 531 ofregion of interest 530) falls within the feature density selectioncriteria for recognition module D (a face recognition module), as thefeature density and distribution reflects that typically found in facialfeatures. For region of interest 520, the preprocessing module 120determines that the feature density value “B” of feature density 521falls within the feature selection criteria range of a recognitionmodule useful in detecting gradients (such as SIFT), as the featuredensity 311 reflects an amount and distribution of features 310generated according to the wrinkles and textures of clothing and bodyparts of a person 220. Finally, for region of interest 510, thepreprocessing module 120 determines that the feature density value “A”of feature density 511 falls within the feature selection criteria rangeof a recognition module useful in detecting edges without much surfacetexture or variations (such as FAST), as the feature density 511reflects the amount and distribution features 310 generated according tothe hard edges and planar, featureless surfaces of building 210 (and itsdoor 212 and windows 211). FIG. 6 illustrates the selected recognitionmodules as assigned to each region of interest 510-540.

In some embodiments, the preprocessing module 120 can assign recognitionmodules to the regions of interest 124 based on additional factorsbeyond feature density. In embodiments where the digital representation121 includes additional information about the scene or othercircumstances under which the digital representation 121 was captured,the preprocessing module 120 can be programmed to derive one or morescene contexts from the additional information.

In embodiments, the system can store pre-defined scene contexts havingattributes to which the preprocessing module 120 can match theadditional information included in the digital representation 121 to acorresponding context. The scene contexts can be embodied as entrieswithin a scene context database indexed according to context attributesand including context data, or as independent data objects havingcontext attributes and context data. The context data of a particularscene context is generally considered to be data or information that caninfluence the selection of a recognition module for one or more regionsof interest 124 to reflect the particular scene context.

In an illustrative example, preprocessing module 120 can determine thata digital representation 121 has been captured within a “natural area”as determined from GPS coordinates (e.g., the GPS coordinates associatedwith the digital representation 121 matches coordinate attributes of anarea associated with a “natural area” scene context). The context dataof the matched “natural area” scene context then indicates that it ismore likely that an object recognition module (e.g., plant recognizers,animal recognizer, etc.) would be more appropriate than an OCR moduleusing one or more of the techniques discussed below. Example types ofdata that can be utilized with respect to deriving scene context includea location, a position, a time, a user identity (e.g., user informationfrom public sources and/or from a subscription or registration with asystem providing the inventive subject matter, a user profile, etc.), anews event, a medical event, a promotion, user preferences, a user'shistorical data, historical data from a plurality of users, or otherdata.

In embodiments, a context data can be in the form of a modificationfactor associated with the scene context. The modification factor servesto modify the process of selecting a recognition module for a region ofinterest 124 to reflect an increased or decreased likelihood that aparticular recognition module is applicable to the digitalrepresentation in the particular context of the scene.

In one aspect of these embodiments, the preprocessing module 120 canapply the modification factor to the feature density selection criteria125 itself and thus modify the criteria that is used with the featuredensity values for the regions of interest 124. For example, themodification factor value can be applied to thresholds or featuredensity ranges applicable to one or more of the recognition modules A-Nsuch that a particular threshold or range is modified. Consequently, arecognition module that would have fallen outside of a threshold orrange for a particular region of interest 124 before the modificationvalue is applied could be found to be within the modified threshold orrange after the application of the modification factor value.

In another aspect of these embodiments, the preprocessing module 120 canapply the modification factor to the calculated feature densities one ormore of the generated region(s) of interest 124 within digitalrepresentation 121. Here, the modified feature densities of the regionsof interest 124 are then used by the preprocessing module 120 as theinputs to the feature density selection criteria 125 to select theappropriate recognition module for each region of interest.

It is contemplated that the two aspects of these embodiments describedabove can be used separately or in combination to modify the recognitionmodule selection process. In these embodiments, the modification factorcan be a linear or non-linear scalar or function applied to the featuredensity selection criteria 125 and/or the feature densities themselvesto result in the modification.

In embodiments, context data can include an identifier of an object oran object class that is highly likely to appear in the digitalrepresentation, and can further include a probability or likelihoodindicator for the object or object class. Based on the probabilityindicator, the preprocessing module 120 can select one or morerecognition modules that are a priori determined to be applicable to theobject or object class. This selection can be in addition to or insteadof the recognition modules selected for the regions of interest 124 viathe feature density selection criteria 125. For instance, in the“natural area” example described above, the object recognition module isselected by the preprocessing module 120 for all regions of interest 124in the digital representation 121 even if the feature density selectioncriteria 125 results in the selection of an OCR module, and this canoverride the selection of the OCR module or, alternatively, be used forthe particular region of interest 124 in combination with the selectedOCR module. In a variation of these embodiments, the object identifierand/or the probability indicator can be used as a “tie-breaker” inselecting the applicable recognition module. For example, the results ofthe feature density selection criteria 125 for a region of interest 124may result in more than one applicable recognition module. To decidewhich of the potential candidate modules to employ, the preprocessingmodule 120 can apply the object (or object class identifier) anddetermine which (if any) of the candidate modules has been a prioridetermined to be applicable to the particular object or object class andselect accordingly. Where more than one candidate module fits theobject/object class, the probability indicator can be applied as aweighting factor for each candidate to determine a winner.

In embodiments, the context data can include an identification of one ormore recognition modules that are to be eliminated from consideration.In these embodiments, the preprocessing module 120 can performerror-detection functions by checking for “false positive” recognitionmodule identification. To do this, the preprocessing module 120 cancheck the identified recognition module(s) in the context data againstthose selected for each region of interest 124 (selected according tothe processes of the inventive subject matter described herein) in thedigital representation 121 and determine if there are any matches. If amatch results, the matching recognition modules can be flagged as errorsby the preprocessing module 120. In embodiments, error messages can begenerated and provided to system administrators via email or other formof notification. In embodiments, the selection process can bere-executed to determine whether the error was a single anomaly or asystemic error for correction and flagged accordingly. In embodiments, adifferent recognition module can be selected to replace the erroneousrecognition module whose feature density selection criteria 125 issatisfied by the feature density (and other characteristics) of theparticular region of interest 124.

As the recognition modules A-N are assigned to the regions of interest124, the preprocessor module 120 can configure the assigned recognitionmodules to process their respective regions. The recognition modules A-Ncan be instructed to process the regions serially or in paralleldepending on the nature of the processing device. In a single processingcore computing device, if desired, the recognition modules A-N can beordered for execution or ranked based on relevance to their regionsbased on matching scores with respect to the selection criteria. Inmulti-core computing devices, the recognition modules can be allocatedto various cores for parallel processing. In other embodiments, therecognition modules can execute their tasks on remote devices includingremote servers, web services, cloud platforms, or even networkinginfrastructure (e.g., switches, see U.S. patent application U.S.2010/0312913 to Wittenschlaeger titled “Hybrid Transport—ApplicationNetwork Fabric Apparatus”, filed Aug. 3, 2010).

The relevance of the regions in selecting an order of execution can befurther affected by other factors such as entered search terms, a userprofile, or other information. For example, in response to auser-entered search query for a type of car in an image, thepreprocessing module 120 can prioritize the execution of recognitionmodules that are most closely related to identifying real-world objectsand delay any OCR or other text-recognition modules. In another example(illustrated further via a use-case example below), a user profile canindicate that a user is visually-impaired and, as such, in executingmodules for an image including text and objects (such as a newspaperpage), the OCR modules can be prioritized over other modules to speed upthe ability for the audio output modules of a reading program to executeand read the text out loud to the user.

In view that each recognition module A-N can be aligned with aphilosophical approach to object recognition processing and that theirassociated recognition algorithms operate best on different classes ofobjects, is should be appreciated that the disclosed preprocessingtechniques can also be leveraged to classify the regions of interestwith respect to a type of object. Therefore, in embodiments, thepreprocessing module 120 can include a region classifier that can beconfigured to classify the regions of interest 124 according to anobject type as a function of attributes derived from the region featuredensity (e.g., raw density, shape, distribution, etc.) and digitalrepresentation (e.g., location, position, context, etc.). Thus, in theseembodiments, the feature density selection criteria 125 can also beconsidered a feature-density-based object type or object classsignature. Object classes can include a face, an animal, a vehicle, adocument, a plant, a building, an appliance, clothing, a body part, atoy, or other type of object. Example attributes that can be leveragedfor the classifier can include interrelationship metrics deriveddirectly from the region feature density, or even among multiple featuredensities across multiple regions of interest (e.g., a geometric metric,a time-based metric, an orientation metric, a distribution metric,etc.). Such an approach is considered advantageous for compound objectshaving multiple parts (e.g., animals, people, vehicles, store shelves,etc.). In the example of FIG. 5 , an interrelationship metric can existbetween the region of interest 530 (corresponding to the face) and theregion of interest 520 (the region corresponding to the person's body)such that, because the region classifier classifies the region ofinterest 530 as likely to be a face (due to the raw density, shape anddistribution of features 310 within ROI 530 being within a certaindegree of similarity to the signature of the “face” object class), thepreprocessing module 120 interprets the region of interest 520 as havinga likelihood of corresponding to the “body” given region of interest520's position relative to the “face” region of interest 530. Additionalinformation that can aid in region classification can relate to thenature of the region, perhaps black on white text, white on black text,color text, classification of font, or other information.

Contemplated region classifiers can include additional roles orresponsibilities beyond classifying regions as relating to specificobject types. One example additional responsibility can includeassigning a likelihood score to a region where the score indicates thatthe region of interest is associated with a class of objects. In someembodiments, the object class likelihood can have a fine level ofgranularity that ranges from a region level down to a pixel level (i.e.,assuming image data). Thus, each pixel in a region of interest caninclude metadata that indicative of the object classes that might berelevant to that pixel. The object class information or metadata can beorder according to a likelihood function and could be organizedaccording a table, linked list, or other suitable data structure.Therefore, the region classifier could be considered a pixel-levelclassifier.

As discussed above, the regions of interest can be associated with oneor more different types of objects represented within the region ofinterest, including physical objects. Of particular interest are regionsof interest that represent at least one printed media (e.g., poster,document, billboard, news paper, book, comic box, magazine, coupon,driver's license, etc.) in the scene. Contemplated printed media caninclude a financial document (e.g., a check, credit card, currency note,etc.), a structured document (e.g., a template-based document, agovernment-issued document, etc.), advertisement media, etc. Thefollowing is a use case illustrative of the incorporation of theinventive subject matter as described herein. In this use case, thesystems and methods of the inventive subject matter are implemented in asystem that helps a visually-impaired person read a newspaper.

Typically, printed newspaper will include sections of text (such as theheadlines, articles, etc.) as well as areas including imagery (e.g.,photographs, advertisements, logos, etc.). In this example, a visuallyimpaired user possesses a smartphone including a camera and that hasbeen equipped with the object data operating system 110 of the inventivesubject matter. As part of the installation process, the user creates auser profile and includes the information that the user is visuallyimpaired, which is stored as context data in the system.

When the user desires to “read” a newspaper, the user holds thesmartphone such that the camera captures at least part of the newspaperpage (the user can first be required to open or otherwise initialize anapplication that invokes the object data operating system 110 to begin).As described above, the preprocessing module 120 receives the image dataand executes the feature identification algorithms 122 (FAST, etc.) onthe image of the newspaper and generates the features, performs theclustering and determines the regions of interest for the image,including regions of interest corresponding to the text on the newspaperpage and regions of interest for the photographs on the newspaper page.Based on the feature density selection criteria 125, the preprocessingmodule 120 determines that the OCR module is applicable to the text andother recognition modules are applicable to various aspects of thephotographs and logos. The preprocessing module 120 applies theavailable context data (i.e., “the user is visually impaired”), whichincludes rules that prioritize the execution of the OCR module. Thus, atext “reader” program within the smartphone can begin reading the textto the user as quickly as possible. If the system does not have thecapability to provide any audio output for the photographs, theexecution of the other modules (corresponding to the recognition ofobjects in the photographs) can be ignored altogether.

As described below in the next section FAST can be used to specificallyidentify regions of interest that represent a document, possiblyincluding a structured document or a financial document. Regions ofinterests that represent structured documents, that is a document of aknown structure, can be processed by a template-drive recognition moduleas discussed below.

The following discussion describes a system for detecting and localizingtext regions in images and videos capturing printed page, books,magazine, mail envelope, and receipt in real-time using a smart phonecamera. The system includes stages for i) identifying text regions fromlow-resolution video frames, ii) generating audio feedback to guide avisually impaired personal to capture the entire text region in thescene, iii) triggering the camera to capture a high-resolutionstill-image of the same scene, iv) recognizing the text regions usingoptical character recognition tools that run on the mobile device or inthe cloud, and v) pronouncing the recognized text using text-to-speech(TTS) module. One aspect of the described technique includes a real-timeaudio guided feedback to capture an acceptable image for the OCR engine.Methods for corner detection, connected component analysis, andparagraph structure test are used in the text detection module. Thealgorithm has been tested on an iPhone device where enhanced performancewas achieved. The usage simplicity and availability of the applicationon smart phones will yield advantages over traditional scanner-based OCRsystems.

Several systems have been proposed in the past to address the need for amobile-based text detection and recognition. One type of previousapproach seeks to localize isolated text in the wild such as trafficsigns or room numbers (or names) in a hallway. Such text detectionsystems help the visually impaired person navigate independently on thestreet or within the workplace. The disclosed approach differs fromprevious approaches by seeking to localize and recognize structured textregions (i.e., regions of interest) such as printed page, magazine,utility bill, and receipt.

Example previous effort that identified text regions include thosedescribed in A. Zandifar, P. R. Duraiswami, A. Chahine and L. S. Davis,“A video based interface to textual information for the visuallyimpaired”, Fourth IEEE International Conference on MultimodalInterfaces, 2002. Unfortunately, the described system lacks mobility asit requires many devices. It also lacks of any audio feedback mechanismor status update, which make it hard for blind people to use.

Furthermore, Ferreira et al. proposed a text detection and recognitionsystem that runs on a personal digital assistant (PDA) (see S. Ferreira,V. Garin, and B. Gosselin, “A text detection technique applied in theframework of a mobile camera-based application,” First InternationalWorkshop on Camera-based Document Analysis and Recognition, 2005).Unfortunately, the Ferreira approach fails to provide real-timefeedback.

The following disclosed system (see FIG. 7 ) uses OCR and TTS tools thatrun on mobile platforms. In addition the disclosed real-time video-basedtext detection algorithm, aids visually impaired people to quicklyunderstand printed text via image capture and analysis. The mainchallenge in blind photography is to assist the visually impaired personin capturing an image that contains an entire text region. The disclosedsystem addresses this issue by utilizing a fast text detection module(e.g., feature invariant identification algorithm) that runs onlow-resolution video frames (e.g., digital representation). Furthermore,a bounding box is placed around the detected text region (e.g., regionof interest) and an audio feedback is provided to the user to indicatethe status of the text in the scene. The system gives verbal feedback,or other audio feedback or tactile feedback, such as “left”, “right”,“forward”, “backward”, “zoom-in”, or “zoom-out” to help the user movethe mobile phone in the space. The system informs the user if thedetected text region touches (or has been cut at) any of the boundariesof the captured scene. When the printed text is at the center of thecaptured scene, a “hold still” audio feedback is sent to the user toeliminate capturing blurry or out-of-focus image. The auto-capturedstill “high-resolution” image is sent to the OCR engine. An example OCRengine that can be leveraged includes those offered by ABBYY® (see URLwww.abbyy.com/mobileocr/). ABBYY is used where it provides enhancedperformance when a five (or greater) megapixels image is used. Thedisclosed system also utilizes a TTS module to speak the recognized textto the user. The system can further enable emailing the captured imageand generated text to the user for future reference.

The previous example references providing auditory, verbal feedback to avisually impaired user. However, alternative feedback modalities arealso contemplated. The feedback to the user can take on non-verbalfeedback, perhaps based on music, audible tempo, or other sounds.Further, the feedback can be visual by providing visual indicators oricons that instruct a user how to position their mobile device. Stillfurther the feedback could include tactile feedback, perhaps in the formof a vibration on the mobile device, which indicates when the device isposition properly. A more specific example could include using a cellphones vibration capability to indicate when the device is incorrectlypositioned. As the user nears an optimal position, the strength (e.g.,frequency, amplitude, etc.) of the vibration might decrease until theoptimal position is achieved.

The system of FIG. 7 has three main modules. A first module includevideo-based text detection module where texture features of printed textare used to identify text-candidate regions. A paragraph structure testis also utilized to confirm text detection. A second module includes anaudio feedback module where a verbal feedback is provided to the user ifthe text region is cropped or a mobile phone displacement is required.The third module enables the capture of high-resolution still-image,which is sent to the OCR tool. The generated text is spoken to the uservia a TTS component.

FIG. 8 presents a block diagram of the video-based text detectionmodule. Note that the disclosed algorithm is designed to detect printedtext with sufficient contrast to the background. In some embodiments,the algorithm assumes that the target text shows strong-texturedcharacteristics (small font size) and forms several text lines. Toaddress the first assumption, the FAST corner detection algorithm (e.g.,feature identification algorithm) is utilized to find texture regions inthe video frames given that the user is pointing the mobile phone cameraat printed text. The generated corner map is cut to 8×8 windows where acorner density map is found by averaging the number of corners in eachwindow. The density map is binarized using a global threshold andfurther processed using connected component analysis and small regionelimination. A minimum-bounding box (e.g., a region of interest) isfitted around the main regions, which are tested for paragraph structureand audio feedback is communicated to the user.

FIG. 9 shows a text region of image data with the FAST corner detectionmap. The original implementation of the FAST algorithm has apost-processing method for non-maximal suppression, that is, toeliminate low-confidence corner points or minimize the number ofdetected corners in small neighborhood. However, a high-density cornermap is a favorable feature when text detection is considered. Thedocument in FIG. 9 is captured using 640×480 pixels, which shows 43,938corner points. Note that the FAST algorithm is designed to detectcorners in gray-scale images. This illustrates a possible need for colorspace conversion (or color to gray scale conversion) if the input imageis captured in RGB (or BGRA in iPhones). More preferred embodiments areoptimized by converting BGRA to YUV where the Y-channel is used forcorner detection.

In this module, the corner density map is generated by block processingthe corner map based on 8×8 pixel window as shown in FIGS. 10A-10C. Thedigital count (gray-level) of any small window in the corner density map(FIG. 10B) resembles the number of corners in the corresponding windowin the corner map in FIG. 10A. FIG. 10C shows a binary map of FIG. 10B.Note that the size of FIGS. 10A-10C is 80×60 pixels in the currentimplementation. A connected-component labeling stage is also included inthe proposed module to identify the number of text-candidate regions inthe view. It also helps identifying small regions that can be eliminatedor posing geometrical restrictions on text-candidate regions.

The paragraph structure test (e.g., feature density selection criteria)verifies the text-candidate region based on an assumption that a textregion consists of a sentence, multiple sentences, or a paragraph. Thatis, if any text-candidate region is considered by itself, its structureshould generate a set of peaks and valleys of intensity values (e.g.,region feature density attributes) if averaged in the horizontal orvertical direction (profile projection). The characteristics of thesepeaks and valleys of the feature density substructure (shown in FIGS.11A-11C) indicate the font size used in the written text and thedistances between the lines. FIG. 11A corresponds to the input image,FIG. 11B to the normalized vertical projection and FIG. 11C to thenormalized horizontal projection. The Run-Length Encoding (RLE)technique is applied to the projection vectors where the mean andstandard deviation (STD) of the resulting RLE coefficients are used toperform the paragraph structure test.

One objective of the audio feedback module is to help the user to locatethe detected text region so that it is in the camera view. That is, toaid the user to position the text region so that it does not touch anyof the image borders and has sufficient size. As shown in FIG. 12 , thealgorithm firstly checks if the size of the detected-text region is lessthan an empirically selected threshold (minTextArea), if yes, a “zoomin” audio track is played and the algorithm proceeds to analyze the nextframe. However, if the area of the text-candidate region is accepted,the text-candidate boarders are compared to the video frame boundariesto generate a suitable feedback as shown in FIG. 12 . Finally, if thetext-candidate area has adequate size and is not cropped/clipped, the“hold still” audio track feedback is played while the still-imagecapture module is initialized.

FIGS. 13A-13C illustrate three visual and audio feedback scenarios asgiven in a current implementation. FIG. 13A corresponds to the zoom outstate where the text-candidate borders touch the frame boundaries whileFIG. 13B advices the user to move the mobile device forward. Lastly,FIG. 13C resembles the detection of text-region where a “hold still”feedback is given.

The video-based text detection and audio feedback modules simultaneouslyrun to help the user locating the target text region (e.g., the regionof interest). Once the text-candidate region satisfies the conditionsfor capturing still image, the camera is triggered to capture ahigh-resolution still image as shown in FIG. 14 . Capturing astill-image (FIG. 15A) also requires that the mobile phone be heldstable to minimize motion blur. The borders of the target text regionthat have been detected in the low-resolution video frame are scaled tomatch the high-resolution still image where the region of interest isextracted. The cut region, or the entire still-image, is sent to the OCRtool (e.g., recognition module). The current implementation leverages amobile- and general-OCR module from APPYY to run the OCR on a mobilephone. FIG. 15B illustrates a real-time interaction between amobile-based OCR as well as cloud-based system. Note that an audiofeedback is provided to the user about the OCR progress if needed. Therecognized text is displayed as shown in FIG. 15C and is also sent to aTTS tool. The user hears the recognized text through the mobile phonespeakers.

It should be apparent to those skilled in the art that many moremodifications besides those already described are possible withoutdeparting from the inventive concepts herein. The inventive subjectmatter, therefore, is not to be restricted except in the spirit of theappended claims. Moreover, in interpreting both the specification andthe claims, all terms should be interpreted in the broadest possiblemanner consistent with the context. In particular, the terms “comprises”and “comprising” should be interpreted as referring to elements,components, or steps in a non-exclusive manner, indicating that thereferenced elements, components, or steps may be present, or utilized,or combined with other elements, components, or steps that are notexpressly referenced. Where the specification claims refers to at leastone of something selected from the group consisting of A, B, C . . . andN, the text should be interpreted as requiring only one element from thegroup, not A plus N, or B plus N, etc.

1-37. (canceled)
 38. An object data processing system comprising: atleast one processor configured to execute: at least one of a pluralityof recognition modules stored on at least one non-transitorycomputer-readable storage medium, wherein each of the plurality ofrecognition modules comprises one or more recognition algorithms and oneor more feature density selection criteria; and data preprocessing codeexecuted by at least one processor, the data preprocessing codeconfigured to: obtain a digital representation of a scene from one ormore sensors wherein the scene comprises one or more objects; determineone or more regions of interest within the digital representation byapplying a preprocessing feature identification algorithm, wherein theone or more regions of interest comprises a characteristic featuredensity; and assign at least one of the plurality of recognition modulesto each region of interest as a function of the characteristic featuredensity and the one or more feature density selection criteria of theplurality of recognition modules.
 39. The system of claim 38, whereinthe preprocessing feature identification algorithm comprises at leastone of the following invariant feature identification algorithms: FAST,SIFT, FREAK, BRISK, Harris, DAISY, and MSER.
 40. The system of claim 38,wherein the at least one recognition algorithm includes at least one ofthe following: edge detection algorithm, corner detection algorithm,saliency map algorithm, curve detection algorithm, a text onidentification algorithm, a wavelets algorithm, a template drivenalgorithm, a face recognition algorithm, an optical characterrecognition (OCR) algorithm, an object recognition algorithm, anautomatic speech recognition (ASR) algorithm, and a support vectormachine (SVM)-based algorithm.
 41. The system of claim 38, wherein thedigital representation comprises at least one of the following types ofdigital data: image data, video data, biometric data, temperature data,pressure data, location data, electrical data, and audio data.
 42. Thesystem of claim 38, wherein the digital representation is obtained usinga device comprising at least one of the following: a smart phone, atablet, a robot, a wearable glass, a toy, a game console, a vehicle, acomputer, a camera, a scanner, and a phablet.
 43. The system of claim38, wherein the one or more sensors comprise a camera, a microphone, athermometer, an anemometer, a touch screen, an accelerometer, agyroscope, a barometer, or a Hall probe.
 44. The system of claim 38,wherein the one or more feature density selection criteria includes oneor more of: a number of features per unit time, a number of features perunit area, a number of features per unit volume, a number of featuresper geometrical area, number of features per unit volume, number offeatures per pixels squared times a depth of field, or number offeatures per unit geometric volume.
 45. The system of claim 38, whereinthe feature density selection criteria comprises one or more of a lowdensity threshold, a high density threshold, or a feature density range.46. The system of claim 38, wherein the characteristic feature densityof the one or more regions of interest comprises a simple scalar densitymetric.
 47. The system of claim 38, wherein the characteristic featuredensity of the one or more regions of interest further comprises one ormore feature substructures.
 48. The system of claim 47, wherein the oneor more feature substructures includes one or more smaller clusters,clusters of invariant features, periodicities of invariant features,block structures of invariant features, frequencies of invariantfeatures, low density regions of invariant features, patterns, contours,variances, distribution widths, centroids, and types of distribution.49. The system of claim 48, wherein the types of distribution compriseone or more of Gaussian or Poisson distributions.
 50. The system ofclaim 38, wherein the region feature density is equal to or exceeds aminimum feature density threshold.
 51. The system of claim 38, whereinthe at least one processor is implemented on a network-accessible serverdevice.
 52. The system of claim 38, further comprising assigning atleast one of the plurality of recognition modules based on context dataof one or more scene contexts.
 53. The system of claim 52, wherein theone or more scene contexts comprises entries in a scene context databaseincluding context data and indexed according to at least one contextattribute.
 54. The system of claim 52, wherein the one or more scenecontext comprises one or more independent data objects having contextattributes and context data.
 55. The system of claim 52, wherein thecontext data used to derive the one or more scene contexts comprises oneor more of a location, a position, a time, a user identity, a newsevent, a medical event, a promotion, a user preference, user historicaldata, or historical data from a plurality of users.
 56. The system ofclaim 52, wherein the context data comprises a modification factorassociated with the scene context.
 57. The system of claim 56, whereinthe modification factor is applied to the feature density selectioncriteria.
 58. The system of claim 56, wherein the modification factor isapplied to the characteristic feature density calculated for one or moreof the generated regions of interest.
 59. A method of classifying anobject, comprising: obtaining a digital representation of a scene fromone or more sensors wherein the scene comprises one or more objects;determining one or more regions of interest within the digitalrepresentation by applying a preprocessing feature identificationalgorithm, wherein the one or more regions of interest comprises acharacteristic feature density; and assigning at least one of aplurality of recognition modules to each region of interest as afunction of the characteristic feature density and the one or morefeature density selection criteria of the plurality of recognitionmodules, wherein the plurality of recognition modules is stored on atleast one non-transitory computer-readable storage medium, and each ofthe plurality of recognition modules comprises one or more recognitionalgorithms and one or more feature density selection criteria.
 60. Anon-transitory computer readable storage medium comprising one or moremachine-readable instructions which, when executed by a processor,perform: obtaining a digital representation of a scene from one or moresensors wherein the scene comprises one or more objects; determining oneor more regions of interest within the digital representation byapplying a preprocessing feature identification algorithm, wherein theone or more regions of interest comprises a characteristic featuredensity; and assigning at least one of a plurality of recognitionmodules to each region of interest as a function of the characteristicfeature density and the one or more feature density selection criteriaof the plurality of recognition modules, wherein the plurality ofrecognition modules is stored on at least one non-transitorycomputer-readable storage medium, and each of the plurality ofrecognition modules comprises one or more recognition algorithms and oneor more feature density selection criteria.